SoroushVahidi/scidocs huggingface dataset:
Learning styles and performance in the introductory programming sequence
(2002)

This paper reports on the implication of different preferred learning styles on students' performance in the introductory programming sequence and on work in progress on how to accommodate these different styles.Students were given a learning styles preference test and then their preferred learning styles were compared to their performance on the exam and the practical programming part of the introductory programming module. There were significant differences in performance between groups of students.This result could lead one to two possible conclusions. One might be that some students' learning styles are more suited to learning programming than others.An alternative explanation is that our current methods of teaching advantage students with certain learning preference styles. We are at present in the process of testing this second assumption by providing students with a wider range of learning materials. We will then see if student performance is improved by using our current results as a baseline for comparison
Human facial expressions as adaptations: Evolutionary questions in facial expression research.
(2001)

The importance of the face in social interaction and social intelligence is widely recognized in anthropology. Yet the adaptive functions of human facial expression remain largely unknown. An evolutionary model of human facial expression as behavioral adaptation can be constructed, given the current knowledge of the phenotypic variation, ecological contexts, and fitness consequences of facial behavior. Studies of facial expression are available, but results are not typically framed in an evolutionary perspective. This review identifies the relevant physical phenomena of facial expression and integrates the study of this behavior with the anthropological study of communication and sociality in general. Anthropological issues with relevance to the evolutionary study of facial expression include: facial expressions as coordinated, stereotyped behavioral phenotypes, the unique contexts and functions of different facial expressions, the relationship of facial expression to speech, the value of facial expressions as signals, and the relationship of facial expression to social intelligence in humans and in nonhuman primates. Human smiling is used as an example of adaptation, and testable hypotheses concerning the human smile, as well as other expressions, are proposed.
A Comparison Between Single-Phase Quasi-  $Z$-Source and Quasi-Switched Boost Inverters
(2015)

The properties of a single-phase quasi Z-source inverter (qZSI) and a single-phase quasi-switched boost inverter (qSBI), both of which are single-stage buck-boost inverters, are investigated and compared. For the same operating conditions, qSBI has the following advantages over qZSI: 1) Three capacitors are saved; 2) the current rating on both of its switches and diodes is lower; 3) its boost factor is higher with an equivalent parasitic effect; and 4) its efficiency is higher. However, qSBI has one more active switch and one more diode than Z-source/ qZSIs. In addition, the capacitor voltage stress of qSBI is higher than that of qZSI. The dc and ac component circuit analysis, impedance design with low-frequency and high-frequency ripples, component stresses, and power loss calculation are presented. A prototype based on a TMS320F28335 DSP is built in order to compare the operating principle of qSBI and qZSI.
A Neural Schema Architecture for Autonomous Robots
(1998)

As autonomous robots become more complex in their behavior, more sophisticated software architectures are required to support the ever more sophisticated robotics software. These software architectures must support complex behaviors involving adaptation and learning, implemented, in particular, by neural networks. We present in this paper a neural based schema [2] software architecture for the development and execution of autonomous robots in both simulated and real worlds. This architecture has been developed in the context of adaptive robotic agents, ecological robots [6], cooperating and competing with each other in adapting to their environment. The architecture is the result of integrating a number of development and execution systems: NSL, a neural simulation language; ASL, an abstract schema language; and MissionLab, a schema-based mission-oriented simulation and robot system. This work contributes to modeling in Brain Theory (BT) and Cogniti ve Psychology, with applications in Distributed Artificial Intell igence (DAI), Autonomous Agents and Robotics. Areas: Robotics, Agent-oriented programming, Neural Nets
Seed priming to alleviate salinity stress in germinating seeds.
(2016)

Salinity is one of the major abiotic stresses that affect crop production in arid and semiarid areas. Seed germination and seedling growth are the stages most sensitive to salinity. Salt stress causes adverse physiological and biochemical changes in germinating seeds. It can affect the seed germination and stand establishment through osmotic stress, ion-specific effects and oxidative stress. The salinity delays or prevents the seed germination through various factors, such as a reduction in water availability, changes in the mobilization of stored reserves and affecting the structural organization of proteins. Various techniques can improve emergence and stand establishment under salt conditions. One of the most frequently utilized is seed priming. The process of seed priming involves prior exposure to an abiotic stress, making a seed more resistant to future exposure. Seed priming stimulates the pre-germination metabolic processes and makes the seed ready for radicle protrusion. It increases the antioxidant system activity and the repair of membranes. These changes promote seed vigor during germination and emergence under salinity stress. The aim of this paper is to review the recent literature on the response of plants to seed priming under salinity stress. The mechanism of the effect of salinity on seed germination is discussed and the seed priming process is summarized. Physiological, biochemical and molecular changes induced by priming that lead to seed enhancement are covered. Plants' responses to some priming agents under salinity stress are reported based on the best available data. For a great number of crops, little information exists and further research is needed.
Signal quality measures for pulse oximetry through waveform morphology analysis.
(2011)

Pulse oximetry has been extensively used to estimate oxygen saturation in blood, a vital physiological parameter commonly used when monitoring a subject's health status. However, accurate estimation of this parameter is difficult to achieve when the fundamental signal from which it is derived, the photoplethysmograph (PPG), is contaminated with noise artifact induced by movement of the subject or the measurement apparatus. This study presents a novel method for automatic rejection of artifact contaminated pulse oximetry waveforms, based on waveform morphology analysis. The performance of the proposed algorithm is compared to a manually annotated gold standard. The creation of the gold standard involved two experts identifying sections of the PPG signal containing good quality PPG pulses and/or noise, in 104 fingertip PPG signals, using a simultaneous electrocardiograph (ECG) signal as a reference signal. The fingertip PPG signals were each 1 min in duration and were acquired from 13 healthy subjects (10 males and 3 females). Each signal contained approximately 20 s of purposely induced artifact noise from a variety of activities involving subject movement. Some unique waveform morphology features were extracted from the PPG signals, which were believed to be correlated with signal quality. A simple decision-tree classifier was employed to arrive at a classification decision, at a pulse-by-pulse resolution, of whether a pulse was of acceptable quality for use or not. The performance of the algorithm was assessed using Cohen's kappa coefficient (κ), sensitivity, specificity and accuracy measures. A mean κ of 0.64 ± 0.22 was obtained, while the mean sensitivity, specificity and accuracy were 89 ± 10%, 77 ± 19% and 83 ± 11%, respectively. Furthermore, a heart rate estimate, extracted from uncontaminated sections of PPG, as identified by the algorithm, was compared with the heart rate derived from an uncontaminated simultaneous ECG signal. The mean error between both heart rate readings was 0.49 ± 0.66 beats per minute (BPM), in comparison to an error value observed without using the artifact detection algorithm of 7.23 ± 5.78 BPM. These results demonstrate that automated identification of signal artifact in the PPG signal through waveform morphology analysis is achievable. In addition, a clear improvement in the accuracy of the derived heart rate is also evident when such methods are employed.
Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systems
(2016)

Product review websites provide an incredible lens into the wide variety of opinions and experiences of different people, and play a critical role in helping users discover products that match their personal needs and preferences. To help address questions that can't easily be answered by reading others' reviews, some review websites also allow users to pose questions to the community via a question-answering (QA) system. As one would expect, just as opinions diverge among different reviewers, answers to such questions may also be subjective, opinionated, and divergent. This means that answering such questions automatically is quite different from traditional QA tasks, where it is assumed that a single 'correct' answer is available. While recent work introduced the idea of question-answering using product reviews, it did not account for two aspects that we consider in this paper: (1) Questions have multiple, often divergent, answers, and this full spectrum of answers should somehow be used to train the system, and (2) What makes a 'good' answer depends on the asker and the answerer, and these factors should be incorporated in order for the system to be more personalized. Here we build a new QA dataset with 800 thousand questions—and over 3.1 million answers—and show that explicitly accounting for personalization and ambiguity leads both to quantitatively better answers, but also a more nuanced view of the range of supporting, but subjective, opinions.
Dual wideband CPW-fed split-ring monopole antenna for WLAN applications
(2010)

This paper presents a dual wideband CPW-fed monopole antenna based on split-ring elements for WLAN (2.4/5 GHz) applications. The proposed split-ring monopole antenna (SRMA) has a compact novel design which provides 13.8% and 55.5% impedance bandwidth at operational frequency bands. Moreover, the antenna exhibits good radiation pattern with predicted gains of 4–6 dBi in the related bands. The analysis and design of the proposed microstrip antenna have been carried out by means of Ansoft HFSS and an antenna prototype was then constructed and tested. Numerical analyses using the simulator and measurement results are also presented in this paper.
Machine learning in the Internet of Things: A semantic-enhanced approach
(2019)

Novel Internet of Things (IoT) applications and services rely on an intelligent understanding of the environment leveraging data gathered via heterogeneous sensors and micro-devices. Though increasingly effective, Machine Learning (ML) techniques generally do not go beyond classification of events with opaque labels, lacking machine-understandable representation and explanation of taxonomies. This paper proposes a framework for semantic-enhanced data mining on sensor streams, amenable to resource-constrained pervasive contexts. It merges an ontology-based characterization of data distributions with non-standard reasoning for a fine-grained event detection. The typical classification problem of ML is treated as a resource discovery by exploiting semantic matchmaking. Outputs of classification are endowed with computer-processable descriptions in standard Semantic Web languages, while explanation of matchmaking outcomes motivates confidence on results. A case study on road and traffic analysis has allowed to validate the proposal and achieve an assessment with respect to state-of-the-art ML algorithms.
Architecture and Implementation of a Java Package for Multiple Input Devices ( MID )
(1999)

A major difficulty in writing Single Display Groupware (co-present collaborative) applications is getting input from multiple devices. We introduce MID, a Java package that addresses this problem and offers an architecture to access advanced events through Java. In this paper, we describe the features, architecture and limitations of MID. We also briefly describe an application that uses MID to get input from multiple mice: KidPad.
Series WP-1207 A Source of Bias in Public Opinion Stability
(2012)

A long acknowledged but seldom addressed problem with political communication experiments concerns the use of captive participants. Study participants rarely have the opportunity to choose information themselves, instead receiving whatever information the experimenter provides. Druckman and his co-authors relax this assumption in the context of an over-time framing experiment focused on opinions about health care policy. Their results dramatically deviate from extant understandings of over-time communication effects. Allowing individuals to choose information for themselves—a common situation on many political issues—leads to the preeminence of early frames and the rejection of later frames. Instead of opinion decay, they find dogmatic adherence to opinions formed in response to the first frame to which participants were exposed (i.e., staunch opinion stability). The effects match those that occur when early frames are repeated multiple times. The results suggest that opinion stability may often reflect biased information seeking. Moreover, the findings have implications for a range of topics including the micro-macro disconnect in studies of public opinion, political polarization, normative evaluations of public opinion, the role of inequality considerations in the debate about health care, and perhaps most importantly, the design of experimental studies of public opinion. *The authors thank Cengiz Erisen, Larry Jacobs, Jon Krosnick, Heather Madonia, Leslie McCall, Spencer Piston, David Sterrett, and seminar participants in Northwestern’s Applied Quantitative Methods Workshop for helpful comments and advice.
Choice of intra-articular injection in treatment of knee osteoarthritis: platelet-rich plasma, hyaluronic acid or ozone options
(2016)

This study was performed to compare the efficacy of treatment in three groups of patients with knee osteoarthritis (OA) given an intra-articular injection of platelet-rich plasma (PRP), hyaluronic acid (HA) or ozone gas. A total of 102 patients with mild–moderate and moderate knee OA who presented at the polyclinic with at least a 1-year history of knee pain and VAS score ≥4 were randomly separated into three groups. Group 1 (PRP group) received intra-articular injection of PRP × 2 doses, Group 2 (HA group) received a single dose of HA, and Group 3 (Ozone group) received ozone × four doses. Weight-bearing anteroposterior–lateral and Merchant’s radiographs of both knees were evaluated. WOMAC and VAS scores were applied to all patients on first presentation and at 1, 3, 6 and 12 months. At the end of the 1st month after injection, significant improvements were seen in all groups. In the 3rd month, the improvements in WOMAC and VAS scores were similar in Groups 1 and 2, while those in Group 3 were lower (p < 0.001). At the 6th month, while the clinical efficacies of PRP and HA were similar and continued, the clinical effect of ozone had disappeared (p < 0.001). At the end of the 12th month, PRP was determined to be both statistically and clinically superior to HA (p < 0.001). In the treatment of mild–moderate knee OA, PRP was more successful than HA and ozone injections, as the application alone was sufficient to provide at least 12 months of pain-free daily living activities. Therapeutic study, Level I.
Polyphonic Music Modelling with LSTM-RTRBM
(2015)

Recent interest in music information retrieval and related technologies is exploding. However, very few of the existing techniques take advantage of the recent advancements in neural networks. The challenges of developing effective browsing, searching and organization techniques for the growing bodies of music collections call for more powerful statistical models. In this paper, we present LSTM-RTRBM, a new neural network model for the problem of creating accurate yet flexible models of polyphonic music. Our model integrates the ability of Long Short-Term Memory (LSTM) in memorizing and retrieving useful history information, together with the advantage of Restricted Boltzmann Machine (RBM) in high dimensional data modelling. Our approach greatly improves the performance of polyphonic music sequence modelling, achieving the state-of-the-art results on multiple datasets.
Improving the effectiveness of peer feedback for learning
(2009)

The present study examined the effectiveness of (a) peer feedback for learning, more specifically of certain characteristics of the content and style of the provided feedback, and (b) a particular instructional intervention to support the use of the feedback. A quasi-experimental repeated measures design was adopted. Writing assignments of 43 students of Grade 7 in secondary education showed that receiving ‘justified’ comments in feedback improves performance, but this effect diminishes for students with better pretest performance. Justification was superior to the accuracy of comments. The instructional intervention of asking assessees to reflect upon feedback after peer assessment did not increase learning gains significantly. 2009 Published by Elsevier Ltd.
Reference-Aware Language Models
(2017)

We propose a general class of language models that treat reference as explicit stochastic latent variables. This architecture allows models to create mentions of entities and their attributes by accessing external databases (required by, e.g., dialogue generation and recipe generation) and internal state (required by, e.g. language models which are aware of coreference). This facilitates the incorporation of information that can be accessed in predictable locations in databases or discourse context, even when the targets of the reference may be rare words. Experiments on three representative applications show our model variants outperform models based on deterministic attention.
Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty
(2010)

Data mining (DM) methodology has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the data used by researchers. This study has proposed a new procedure, based on expanded RFM model by including one additional parameter, joining WRFM-based method to K-means algorithm applied in DM with K-optimum according to Davies– Bouldin Index, and then classifying customer product loyalty in under B2B concept. The developed methodology has been implemented for SAPCO Co. in Iran. The result shows a tremendous capability to the firm to assess his customer loyalty in marketing strategy designed by this company in comparing with random selection commonly used by most companies in Iran. 2009 Elsevier Ltd. All rights reserved.
Document dewarping via text-line based optimization
(2015)

This paper presents a new document image dewarping method that removes geometric distortions in camera-captured document images. The proposed method does not directly use the text-line which has been the most widely used feature for the document dewarping. Instead, we use the discrete representation of text-lines and text-blocks which are the sets of connected components. Also, we model the geometric distortions caused by page curl and perspective view as the generalized cylindrical surfaces and camera rotation respectively. With these distortion models and the discrete representation of the features, we design a cost function whose minimization yields the parameters of the distortion model. In the cost function, we encode the properties of the pages such as text-block alignment, linespacing, and the straightness of text-lines. By describing the text features using the sets of discrete points, the cost function can be easily defined and efficiently solved by Levenberg–Marquadt algorithm. Experiments show that the proposed method works well for the various layouts and curved surfaces, and compares favorably with the conventional methods on the standard dataset. & 2015 Elsevier Ltd. All rights reserved.
Discipline and practices of TDD: (test driven development)
(2003)

This panel brings together practitioners with experience in Agile and XP methodologies to discuss the approaches and benefits of applying Test Driven Development (TDD). The goal of TDD is clean code that works. The mantra of TDD is: write a test; make it run; and make it right. Open questions to be addressed by the panel include: - How are TDD approaches to be applied to databases, GUIs, and distributed systems? What are the quantitative benchmarks that can demonstrate the value of TDD, and what are the best approaches to solve the ubiquitous issue of scalability.
Ontology-based decision making on uncontrolled intersections and narrow roads
(2015)

Many Advanced Driver Assistance Systems (ADAS) have been developed to improve car safety. However, it is still a challenging problem to make autonomous vehicles to drive safely on urban streets such as uncontrolled intersections (without traffic lights) and narrow roads. In this paper, we introduce a decision making system that can assist autonomous vehicles at uncontrolled intersections and narrow roads. We constructed a machine understandable ontology-based Knowledge Base, which contains maps and traffic regulations. The system makes decisions in comply with traffic regulations such as Right-Of-Way rules when it receives a collision warning signal. The decisions are sent to a path planning system to change the route or stop to avoid collisions.
Using Ontology-Based Traffic Models for More Efficient Decision Making of Autonomous Vehicles
(2008)

The paper describes how a high-level abstract world model can be used to support the decision-making process of an autonomous driving system. The approach uses a hierarchical world model and distinguishes between a low-level model for the trajectory planning and a high-level model for solving the traffic coordination problem. The abstract world model used in the CyberCars-2 project is presented. It is based on a topological lane segmentation and introduces relations to represent the semantic context of the traffic scenario. This makes it much easier to realize a consistent and complete driving control system, and to analyze, evaluate and simulate such a system.
The Semantic Web Revisited
(2006)

The article included many scenarios in which intelligent agents and bots undertook tasks on behalf of their human or corporate owners. Of course, shopbots and auction bots abound on the Web, but these are essentially handcrafted for particular tasks: they have little ability to interact with heterogeneous data and information types. Because we haven't yet delivered large-scale, agent-based mediation, some commentators argue that the semantic Web has failed to deliver. We argue that agents can only flourish when standards are well established and that the Web standards for expressing shared meaning have progressed steadily over the past five years